Computerized recognition of handwritten math equations is a useful tool in many scenarios including educational and office automation environments. Typically, recognizing a math equation occurs in two stages, comprising a first, symbol segmentation and recognition stage, and a second, structural analysis stage. In general, the symbol segmentation and recognition stage determines possible boundaries for mathematical symbols, and recognizes the symbols as candidates. With the symbol boundaries and candidates, structural analysis is performed, which in general analyzes the equation's spatial structure and semantic construction.
However, the first stage suffers from recognition accuracy problems, in part because handwriting recognition has an inherent difficulty in discriminating between certain symbols. For example, handwriting recognizers often have difficulty differentiating between ‘6’ and ‘b’, ‘a’ and ‘α’, ‘w ’and ‘ω’, and so forth. As computer users generally desire better and better recognition systems, any improvement in symbol recognition accuracy is beneficial.